0

Explore Products

Best Sellers

New Releases

Books

Videos

Audiobooks

Free Learning

Apache Spark Deep Learning Cookbook

You're reading from Apache Spark Deep Learning Cookbook Over 80 best practice recipes for the distributed training and deployment of neural networks using Keras and TensorFlow

Product type Paperback

Published in Jul 2018

Publisher Packt

ISBN-13 9781788474221

Length 474 pages

Edition 1st Edition

Languages

Scala

Tools

Apache Spark

Concepts

Deep Learning

Authors (2):

Ahmed Sherif

Amrith Ravindra

View More author details

Table of Contents (15) Chapters

Preface

1. Setting Up Spark for Deep Learning Development FREE CHAPTER

2. Creating a Neural Network in Spark

3. Pain Points of Convolutional Neural Networks

4. Pain Points of Recurrent Neural Networks

5. Predicting Fire Department Calls with Spark ML

6. Using LSTMs in Generative Networks

7. Natural Language Processing with TF-IDF

8. Real Estate Value Prediction Using XGBoost

9. Predicting Apple Stock Market Cost with LSTM

10. Face Recognition Using Deep Convolutional Networks

11. Creating and Visualizing Word Vectors Using Word2Vec

12. Creating a Movie Recommendation Engine with Keras

13. Image Classification with TensorFlow on Spark

14. Other Books You May Enjoy

Leave a review - let other readers know what you think

Validating array for optimal neural network performance

A little bit of validation goes a long way in ensuring that our array is normalized for optimal performance within our upcoming neural network.

Getting ready

This section will require a bit of numpy magic using the numpy.stack() function.

How to do it...

The following steps walk through validating that our array has been normalized.

Execute the following step to print the mean and standard deviation of array inputs:

print('standard deviation')
print(round(X[:,0].std(axis=0),0))
print('mean...

The rest of the chapter is locked

Register for a free Packt account to unlock a world of extra content!

A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.

Unlock this book and the full library FREE for 7 days

Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of

Start free trial

Renews at €18.99/month. Cancel anytime

Authors (2)

Ahmed Sherif

Ahmed Sherif

Ahmed Sherif is a data scientist who has worked with data in various roles since 2005. He started off with BI solutions and transitioned to data science in 2013. In 2016, he obtained a master's in Predictive Analytics from Northwestern University, where he studied the science and application of machine learning and predictive modeling using both Python and R. Lately, he has been developing machine learning and deep learning solutions on the cloud using Azure. In 2016, he published his first book, Practical Business Intelligence. He currently works as a Technology Solution Profession in Data and AI for Microsoft.

See other products by Ahmed Sherif

Ravindra

Ravindra

Amrith Ravindra is a machine learning enthusiast who holds degrees in electrical and industrial engineering. While pursuing his masters, he dove deeper into the world of machine learning and developed a love for data science. Graduate-level courses in engineering gave him the mathematical background to launch himself into a career in machine learning. He met Ahmed Sherif at a local data science meetup in Tampa. They decided to put their brains together to write a book on their favorite machine learning algorithms. He hopes this book will help him achieve his ultimate goal of becoming a data scientist and actively contributing to machine learning.

See other products by Ravindra

Other recommended products

Related to this chapter

Machine Learning with Apache Spark Quick Start Guide

Machine Learning with Apache Spark Quick Start Guide

Machine Learning with Apache Spark provides a hands-on introduction to Big Data and Advanced Analytics. In a world driven by mass data creation and consumption, this book combines the latest scalable technologies with advanced analytical algorithms using real-world use-cases in order to derive actionable insights from Big Data in real-time.

Big Data Analysis with Python

Big Data Analysis with Python

Processing big data in real time is challenging due to scalability, information inconsistency, and fault tolerance. Big Data Analysis with Python teaches you how to use tools that can control the data avalanche for you. With this book, you'll learn effective techniques to aggregate data into useful dimensions for posterior analysis, extract statistical measurements, and transform datasets into features for other systems.

Apr 2019 9h 12m

Advanced Deep Learning with R

Advanced Deep Learning with R

This book will help readers to apply deep learning algorithms in R using advanced examples. You will cover variants of neural network models such as ANN, CNN, RNN, LSTM, and more using expert techniques. Readers will make use of popular deep learning libraries such as Keras-R, Tensorflow-R, and more to implement AI models.

Dec 2019 11h 44m

Advanced Deep Learning with R

Advanced Deep Learning with R

This book will help readers to apply deep learning algorithms in R using advanced examples. You will cover variants of neural network models such as ANN, CNN, RNN, LSTM, and more using expert techniques. Readers will make use of popular deep learning libraries such as Keras-R, Tensorflow-R, and more to implement AI models.

Dec 2019 11h 44m

Advanced Deep Learning with R

Advanced Deep Learning with R

This book will help readers to apply deep learning algorithms in R using advanced examples. You will cover variants of neural network models such as ANN, CNN, RNN, LSTM, and more using expert techniques. Readers will make use of popular deep learning libraries such as Keras-R, Tensorflow-R, and more to implement AI models.

Dec 2019 11h 44m

Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide

Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide

Cognitive Toolkit is one of the most popular and recently open sourced deep learning toolkit by Microsoft. Cognitive Toolkit is used to train fast and effective deep learning models. This book will be a quick introduction to using Cognitive Toolkit and will teach you how to train and validate different types of neural networks.

Mar 2019 6h 56m

PySpark Cookbook

PySpark Cookbook

This cookbook presents recipes on leveraging the power of Python and putting it to use in the Apache Spark ecosystem. By the end of this book, you will be able to solve any problem associated with building effective, data-intensive applications and performing machine learning and structured streaming using PySpark.

Jun 2018 11h 0m

PySpark Cookbook

PySpark Cookbook

This cookbook presents recipes on leveraging the power of Python and putting it to use in the Apache Spark ecosystem. By the end of this book, you will be able to solve any problem associated with building effective, data-intensive applications and performing machine learning and structured streaming using PySpark.

Jun 2018 11h 0m

PySpark Cookbook

PySpark Cookbook

This cookbook presents recipes on leveraging the power of Python and putting it to use in the Apache Spark ecosystem. By the end of this book, you will be able to solve any problem associated with building effective, data-intensive applications and performing machine learning and structured streaming using PySpark.

Jun 2018 11h 0m

Python Machine Learning Workbook for Beginners

Python Machine Learning Workbook for Beginners

Through a series of machine learning and data science projects, this book represents a beginner-friendly crash course to Python’s practical application in businesses and your own career.

Mar 2021 9h 18m

Applied Deep Learning with Keras

Applied Deep Learning with Keras

Applied Deep Learning with Keras takes you from a basic knowledge of machine learning and Python to an expert understanding of applying Keras to develop efficient deep learning solutions. This book teaches you new techniques to handle neural networks, and in turn, broadens your options as a data scientist.

Apr 2019 13h 44m

Applied Deep Learning with Keras

Applied Deep Learning with Keras

Applied Deep Learning with Keras takes you from a basic knowledge of machine learning and Python to an expert understanding of applying Keras to develop efficient deep learning solutions. This book teaches you new techniques to handle neural networks, and in turn, broadens your options as a data scientist.

Apr 2019 13h 44m

Personalised recommendations for you

Based on your interests and search pattern

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m